Information Compression and Performance Evaluation of Tic-Tac-Toe's Evaluation Function Using Singular Value Decomposition

TL;DR

This paper explores using singular value decomposition to compress the evaluation function of Tic-Tac-Toe, showing that significant information reduction is possible without major performance loss, and compares different decomposition methods.

Contribution

It introduces a novel application of SVD and Tucker decomposition to compress game evaluation functions, analyzing their impact on game performance.

Findings

70% reduction in evaluation function information without performance loss

HOSVD outperforms simple SVD at the same compression ratio

Compression methods can effectively optimize game strategies

Abstract

We approximated the evaluation function for the game Tic-Tac-Toe by singular value decomposition (SVD) and investigated the effect of approximation accuracy on winning rate. We first prepared the perfect evaluation function of Tic-Tac-Toe and performed low-rank approximation by considering the evaluation function as a ninth-order tensor. We found that we can reduce the amount of information of the evaluation function by 70% without significantly degrading the performance. Approximation accuracy and winning rate were strongly correlated but not perfectly proportional. We also investigated how the decomposition method of the evaluation function affects the performance. We considered two decomposition methods: simple SVD regarding the evaluation function as a matrix and the Tucker decomposition by higher-order SVD (HOSVD). At the same compression ratio, the strategy with the approximated evaluation function obtained by HOSVD exhibited a significantly higher winning rate than that obtained by SVD. These results suggest that SVD can effectively compress board game strategies and an optimal compression method that depends on the game exists.